RulePlanner: All-in-One Reinforcement Learner for Unifying Design Rules in 3D Floorplanning

• URL: http://arxiv.org/abs/2601.22476v1
• Date: Fri, 30 Jan 2026 02:41:48 GMT
• Title: RulePlanner: All-in-One Reinforcement Learner for Unifying Design Rules in 3D Floorplanning
• Authors: Ruizhe Zhong, Xingbo Du, Junchi Yan,
• Abstract summary: Floorplanning determines the coordinate and shape of each module in Integrated Circuits.<n>Current methods are only capable of handling specific and limited design rules.<n>We propose an all-in-one deep reinforcement learning-based approach to tackle these challenges.
• Score: 49.812879456944984
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Floorplanning determines the coordinate and shape of each module in Integrated Circuits. With the scaling of technology nodes, in floorplanning stage especially 3D scenarios with multiple stacked layers, it has become increasingly challenging to adhere to complex hardware design rules. Current methods are only capable of handling specific and limited design rules, while violations of other rules require manual and meticulous adjustment. This leads to labor-intensive and time-consuming post-processing for expert engineers. In this paper, we propose an all-in-one deep reinforcement learning-based approach to tackle these challenges, and design novel representations for real-world IC design rules that have not been addressed by previous approaches. Specifically, the processing of various hardware design rules is unified into a single framework with three key components: 1) novel matrix representations to model the design rules, 2) constraints on the action space to filter out invalid actions that cause rule violations, and 3) quantitative analysis of constraint satisfaction as reward signals. Experiments on public benchmarks demonstrate the effectiveness and validity of our approach. Furthermore, transferability is well demonstrated on unseen circuits. Our framework is extensible to accommodate new design rules, thus providing flexibility to address emerging challenges in future chip design. Code will be available at: https://github.com/Thinklab-SJTU/EDA-AI

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